Inhibitors tethered near the acetylcholinesterase active site serve as molecular rulers of the peripheral and acylation sites

The acetylcholinesterase (AChE) active site consists of a narrow gorge with two separate ligand binding sites: an acylation site (or A-site) at the bottom of the gorge where substrate hydrolysis occurs and a peripheral site (or P-site) at the gorge mouth. AChE is inactivated by organophosphates as they pass through the P-site and phosphorylate the catalytic serine in the A-site. One strategy to protect against organophosphate inactivation is to design cyclic ligands that will bind specifically to the P-site and block the passage of organophosphates but not acetylcholine. To accelerate the process of identifying cyclic compounds with high affinity for the AChE P-site, we introduced a cysteine residue near the rim of the P-site by site-specific mutagenesis to generate recombinant human H287C AChE. Compounds were synthesized with a highly reactive methanethiosulfonyl substituent and linked to this cysteine through a disulfide bond. The advantages of this tethering were demonstrated with H287C AChE modified with six compounds, consisting of cationic trialkylammonium, acridinium, and tacrine ligands with tethers of varying length. Modification by ligands with short tethers had little effect on catalytic properties, but longer tethering resulted in shifts in substrate hydrolysis profiles and reduced affinity for acridinium affinity resin. Molecular modeling calculations indicated that cationic ligands with tethers of intermediate length bound to the P-site, whereas those with long tethers reached the A-site. These binding locations were confirmed experimentally by measuring competitive inhibition constants KI2 for propidium and tacrine, inhibitors specific for the P- and A-sites, respectively. Values of KI2 for propidium increased 30- to 100-fold when ligands had either intermediate or long tethers. In contrast, the value of KI2 for tacrine increased substantially only when ligands had long tethers. These relative changes in propidium and tacrine affinities thus provided a sensitive molecular ruler for assigning the binding locations of the tethered cations.     

QSAR of multiple mutated antibodies

The aim of this study was to develop predictive quantitative structure-activity relationship (QSAR) modeling for antibody-peptide interactions. A small single chain antibody library was designed and manufactured around the murine anti-p24 (HIV-1) monoclonal antibody CB4-1 by use of statistical molecular design (SMD) principles and site directed mutagenesis, and its affinity for a p24 derived antigen was determined by fluorescence polarization. A satisfactory QSAR model (Q(2) = 0.74, R(2) = 0.88) was derived by correlating the affinity data to physicochemical property scales of the amino acids varied in the library. The model explains most of the antibody-antigen interactions of the studied set, and provides insights into the molecular mechanism involved in antigen binding.     

Prediction of indirect interactions in proteins

Background  

Both direct and indirect interactions determine molecular recognition of ligands by proteins. Indirect interactions can be defined as effects on recognition controlled from distant sites in the proteins, e.g. by changes in protein conformation and mobility, whereas direct interactions occur in close proximity of the protein's amino acids and the ligand. Molecular recognition is traditionally studied using three-dimensional methods, but with such techniques it is difficult to predict the effects caused by mutational changes of amino acids located far away from the ligand-binding site. We recently developed an approach, proteochemometrics, to the study of molecular recognition that models the chemical effects involved in the recognition of ligands by proteins using statistical sampling and mathematical modelling.     

Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling

Background  

Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis.     

Identification of the binding pocket for the TRH peptide in the melanocortin 1 receptor

We found recently that thyrotropin-releasing hormone (TRH) acts as a selective agonist on the melanocortin MC₁ receptor. While the TRH was capable of fully activating the MC₁ receptor, it did not interact with any of the other MSH peptide binding G-protein coupled melanocortin receptor subtypes MC_3-5. The MC₁ receptor is a promising target for the development of anti-inflammatory and immuno-modulatory drugs, and it was of wide interest to explore the binding site of the TRH in this receptor. Here we have investigated the binding of TRH to MC₁/MC₃ chimeric receptors and used a partial least squares (PLS) modelling approach for the data evaluation. Statistically valid PLS models (R² = 0.80; Q² = 0.66) were obtained explaining the contribution of the amino acid sequence parts of the receptor chimeras for the binding of TRH. By using the variable importances in the projection (VIPs) deduced from the PLS-model, it was revealed that the transmembrane (TM) regions TM1 and TM2/TM3 contribute the most to the TRH binding. The TM6/TM7 also had a significant influence on the binding. Moreover, it was revealed that an interaction between TM1 and TM6/TM7 of the receptor contributed to the binding of TRH. The data are in full agreement with a 3D model of a TRH peptide and MC₁ receptor complex and validates the location of the TRH ligand binding pocket between the TM1, TM2 and TM7 of the receptor.